# --------------------------------------------------------  
# Fast R-CNN  
# Copyright (c) 2015 Microsoft  
# Licensed under The MIT License [see LICENSE for details]  
# Written by Ross Girshick  
# --------------------------------------------------------  
  
import numpy as np  
import os  
from os.path import join as pjoin  
#from distutils.core import setup  
from setuptools import setup  
from distutils.extension import Extension  
from Cython.Distutils import build_ext  
import subprocess  
  
#change for windows, by MrX  
nvcc_bin = 'nvcc.exe'  
lib_dir = 'lib/x64'  
  
def find_in_path(name, path):  
    "Find a file in a search path"  
    # Adapted fom  
    # http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/  
    for dir in path.split(os.pathsep):  
        binpath = pjoin(dir, name)  
        if os.path.exists(binpath):  
            return os.path.abspath(binpath)  
    return None  
  
  
def locate_cuda():  
    """Locate the CUDA environment on the system 
 
    Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64' 
    and values giving the absolute path to each directory. 
 
    Starts by looking for the CUDAHOME env variable. If not found, everything 
    is based on finding 'nvcc' in the PATH. 
    """  
  
    # first check if the CUDAHOME env variable is in use  
    if 'CUDA_PATH' in os.environ:  
        home = os.environ['CUDA_PATH']  
        print("home = %s\n" % home)  
        nvcc = pjoin(home, 'bin', nvcc_bin)  
    else:  
        # otherwise, search the PATH for NVCC  
        default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin')  
        nvcc = find_in_path(nvcc_bin, os.environ['PATH'] + os.pathsep + default_path)  
        if nvcc is None:  
            raise EnvironmentError('The nvcc binary could not be '  
                'located in your $PATH. Either add it to your path, or set $CUDA_PATH')  
        home = os.path.dirname(os.path.dirname(nvcc))  
        print("home = %s, nvcc = %s\n" % (home, nvcc))  
  
  
    cudaconfig = {'home':home, 'nvcc':nvcc,  
                  'include': pjoin(home, 'include'),  
                  'lib64': pjoin(home, lib_dir)}  
    for k, v in cudaconfig.iteritems():  
        if not os.path.exists(v):  
            raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v))  
  
    return cudaconfig  
CUDA = locate_cuda()  
  
  
# Obtain the numpy include directory.  This logic works across numpy versions.  
try:  
    numpy_include = np.get_include()  
except AttributeError:  
    numpy_include = np.get_numpy_include()  
  
  
def customize_compiler_for_nvcc(self):  
    """inject deep into distutils to customize how the dispatch 
    to cl/nvcc works. 
 
    If you subclass UnixCCompiler, it's not trivial to get your subclass 
    injected in, and still have the right customizations (i.e. 
    distutils.sysconfig.customize_compiler) run on it. So instead of going 
    the OO route, I have this. Note, it's kindof like a wierd functional 
    subclassing going on."""  
  
    # tell the compiler it can processes .cu  
    #self.src_extensions.append('.cu')  
  
      
    # save references to the default compiler_so and _comple methods  
    #default_compiler_so = self.spawn   
    #default_compiler_so = self.rc  
    super = self.compile  
  
    # now redefine the _compile method. This gets executed for each  
    # object but distutils doesn't have the ability to change compilers  
    # based on source extension: we add it.  
    def compile(sources, output_dir=None, macros=None, include_dirs=None, debug=0, extra_preargs=None, extra_postargs=None, depends=None):  
        postfix=os.path.splitext(sources[0])[1]  
          
        if postfix == '.cu':  
            # use the cuda for .cu files  
            #self.set_executable('compiler_so', CUDA['nvcc'])  
            # use only a subset of the extra_postargs, which are 1-1 translated  
            # from the extra_compile_args in the Extension class  
            postargs = extra_postargs['nvcc']  
        else:  
            postargs = extra_postargs['cl']  
  
  
        return super(sources, output_dir, macros, include_dirs, debug, extra_preargs, postargs, depends)  
        # reset the default compiler_so, which we might have changed for cuda  
        #self.rc = default_compiler_so  
  
    # inject our redefined _compile method into the class  
    self.compile = compile  
  
  
# run the customize_compiler  
class custom_build_ext(build_ext):  
    def build_extensions(self):  
        customize_compiler_for_nvcc(self.compiler)  
        build_ext.build_extensions(self)  
  
  
ext_modules = [  
    # unix _compile: obj, src, ext, cc_args, extra_postargs, pp_opts  
    Extension(  
        "bbox",  
        sources=["bbox.pyx"],  
        #define_macros={'/LD'},  
        #extra_compile_args={'cl': ['/link', '/DLL', '/OUT:cython_bbox.dll']},  
        #extra_compile_args={'cl': ['/LD']},  
        extra_compile_args={'cl': []},  
        include_dirs = [numpy_include]  
    ),  
    Extension(  
        "cpu_nms",  
        sources=["cpu_nms.pyx"],  
        extra_compile_args={'cl': []},  
        include_dirs = [numpy_include],  
    ),  
    # Extension(  
    #    'pycocotools._mask',  
    #    sources=['pycocotools\\maskApi.c', 'pycocotools\\_mask.pyx'],  
    #    include_dirs = [numpy_include, 'pycocotools'],  
    #    extra_compile_args={'cl': []},  
    #),  
    #Extension(   # just used to get nms\gpu_nms.obj  
    #    "nms.gpu_nms",  
    #    sources=['nms\\gpu_nms.pyx'],  
    #    language='c++',  
    #    extra_compile_args={'cl': []},  
    #    include_dirs = [numpy_include]  
    #),  
]  
  
setup(  
    name='frcnn',  
    ext_modules=ext_modules,  
    # inject our custom trigger  
    cmdclass={'build_ext': custom_build_ext},  
)  